Many industrial processes are highly complex, involving many devices, different pieces of equipment and sub-processes. This gives rise to many operating or process variables that must be monitored and analysed for online or offline fault detection to be performed and any process deviation to be understood, so that problems can be addressed, and similar problems avoided or prevented from recurring in future. Monitoring an industrial process therefore includes monitoring the performance of various targets, wherein a given target may be a piece of equipment or a process condition.
Principal Component Analysis (PCA) is a monitoring analysis tool involving multivariate statistical techniques that may be applied to monitoring industrial processes that involve many variables. Using mathematical procedures, PCA transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, while each succeeding principal component accounts for the remaining variability with decreasing magnitude. PCA may therefore be considered as revealing the internal structure of data in a way which best explains variance in the data. PCA may be used to derive other performance indicators such as Hotelling's T-square Deviation, Squared Prediction Error (SPE), and Scores and Loadings information from the PCA itself.
Currently, PCA has been used to derive models that replicate the best operating practices of specific industrial processes. A typical best-practice model derived from PCA may be displayed on an Advanced Area Monitoring (AAM) Plot as a bound area comprising a set of principal components derived from data values of variables obtained when the industrial process was operating under normal or acceptable conditions, i.e., the bound area represents a normal condition of the industrial process and can be considered a normal-condition area.
In use, an Advanced Area Monitoring (AAM) Plot may be generated using PCA for data values of the variables obtained from an industrial process during a particular period of operation, as shown in FIG. 1. The AAM plot 1000 typically displays a best-practice model 2000 that has earlier been derived for that particular industrial process, while PCA scores information 3000 derived from operations data values of the variables are projected onto the best-practice model 2000. Where the scores information 3000 fall within the best-practice model 2000, it can be considered that the industrial process was operating normally during that period of operation. If the scores information 3000 fall outside the bound area 2000, it can be considered that process deviation or equipment degradation had occurred for that particular period of operation.
Currently, PCA and AAM plots are mostly used only after a fault or process deviation has already occurred in an industrial process, in an attempt to analyse the problem. This is mainly because it is time consuming for the process engineers to prepare data sets of variables for analysis. Also, understanding the results of analysis requires substantial subject expertise in the process engineer in order for meaningful interpretations of the analysis results to be made, and for translating the results of analysis into action to be undertaken by process operators.